The comparative advantage of the ordinary least squares estimator is that it is computationally less demanding. The integration required to evaluate \(\ell _{L^2}(\theta ; \mathbf {y})\)  is rarely available in closed form and requires numerical evaluation. Additionally, for the non-parametric regression, the values of \(\rho \)  and \(\lambda \)  need to be determined. This allows the bootstrapping procedure proposed in [1]} to be implemented efficiently. However, [2]} show that the asymptotic variance of \(\hat{\theta }_{OLS}\)  is larger than for \(\hat{\theta }_{L2}\) , i.e. \(W_E \succcurlyeq 0\) , where \(A \succcurlyeq B\) , means that \(A-B\)  is positive-definite.
